Interpretable models were the default methods in IML for a long time, but with the advent of deep learning the focus shifted to explanatory methods, allowing for more complex models without performance reducing assumptions imposed by many interpretable models. Explanatory methods are post-training applied to the model and can be clustered into model agnostic vs specific and global vs local methods [1]}. Momentarily, attribution methods are the preferred query-class \(\mathcal {Q}\)  by the field for explanatory methods. They measure the importance of a local feature (e.g. pixels in an image or words in a sentence) into an output by means of gradients [2]}, [3]}, [4]}, [5]}, Shapley values [6]}, perturbations [7]}, [8]}, or latent features [9]}. Other query-classes include, e.g., counterfactual explanations [10]}, symbolic meta-models [11]}, or search methods [12]}. Recently, concept-based methods were introduced, testing if a model learned a certain concept which is derived by either human (CAV) [13]} or machine (ACE) [14]}, and allowing for more global explanations in computer vision. Likewise, explanatory methods contain proxy methods such as adversarial examples [15]}, which can be characterized as a subgroup of counterfactual examples.
